P 9.14 24th

Conference on Severe Local Storms

An Examination of Radar and Lightning Characteristics of

the “Atlanta Tornado” of March 14-15, 2008

John M. Trostel*, Jenny L. Matthews, and Colleen Coyle

Severe Storms Research Center, Georgia Tech Research Institute, Atlanta, Georgia

Nicholas W. S. Demetriades

Vaisala, Inc., Tucson, Arizona

1 INTRODUCTION

On the evening of March 14, 2008, an EF2 tornado

swept through the downtown area of Atlanta Georgia.

This event was unusual in that it directly impacted the

downtown area of a major metropolitan area. The

effects of the tornado were immediately broadcast

nationwide as the SEC championship quarterfinals were

being held in the Georgia Dome which lay just south of

the path of the storm. The storm continued through the

heart of downtown Atlanta, causing major damage and

resulting in one fatality. A second, non-tornadic storm

followed the first storm on a slightly more southerly track.

NEXRAD WSR-88D radar data has been examined and

compared with Cloud to Ground (CG) and Cloud to

Cloud (CC) lightning data obtained from the National

Lightning Detection Network (NLDN) for two storms, one

tornadic and one non-tornadic that crossed the Atlanta

metropolitan during this event. A modified version of the

Storm Cell Identification and Tracking (SCIT) algorithm

has been used to correlate specific NLDN flashes with

each cell. Lightning characteristics such as flash rate,

peak current, polarity, and flash type have been

examined for both cells.

The general synoptic situation which generated the

storm cells is described in Section 2. This includes a

short discussion of the surface and upper air maps, as

well as the thermodynamic characteristics of the

atmosphere. The radar characteristics of the storms are

described in Section 3. Data from the National Climate

* Corresponding author address: John M. Trostel, Severe

Storms Research Center, Georgia Tech Research Institute,

Georgia Institute of Technology, Atlanta, GA 30332-0857;

email: john.trostel@gtri.gatech.edu

Data Center (NCDC) NEXRAD archives has been

examined with both the WDSS-II (Weather Decision

Support System – Integrated Information) and the

GR2Analyst software packages. Lightning stroke and

flash data was obtained for these storms from Vaisala

Inc. from the NLDN. This data set is described in

Section 4. The SCIT algorithm is briefly described in

Section 5, along with the modifications made to the

algorithm to enhance it’s usefulness with these storm

cells. The modified SCIT algorithm allowed the

automated association of NLDN flashes with well

defined individual storm cells. The results of the NLDN

and storm cell association and the differing flash

characteristics of each cell are examined in Section 6.

2 SYNOPTIC SUMMARY

The Atlanta Tornado formed under less than ideal

conditions. The tornado developed from one of two

supercells that formed in northeast Alabama and then

moved into northwest Georgia during the evening hours

of March 14, 2008. The two supercells, including one

which produced an EF2 tornado, formed during the

cooler part of Georgia’s main severe weather season.

Severe thunderstorms that take place during this cooler

part of the season or during the November to October

cool season display rather different characteristics than

storms which form during the main tornado season,

from April through June. These cool season storms are

found to most likely develop during the late evening

(Wasula 2004). These nighttime events are also more

likely to exhibit both low convective available potential

energy (CAPE) values, <1000 J/kg, and exist in high

shear environments with storm relative helicity (SRH)

values greater than 200 m2/s

2 (Guyer and Imy 2006).

Due to the differences in parameters between cool and

warm season tornado environments, the environment

leading to a cool season tornado sometimes can look

less threatening than the warm season tornado

environment (Davies 2006). These cool season

tornadoes can, however, be just as deadly as their

warm season counterparts. Therefore, learning how to

accurately predict these storms and understanding the

associated parameters can be very useful.

The tornado that swept through downtown Atlanta on

the evening of March 14, 2008 exhibited many of these

cool season characteristics and presented a somewhat

subtle synoptic situation. With some of the usual

ingredients needed for severe weather in the warm

season missing, the Atlanta tornado is better explained

using cool season severe weather parameters.

The first clue in determining the circumstances

surrounding the development of this tornado was to

examine the CAPE values as well as the SRH values.

Usually, significantly larger values of CAPE and SRH

than were observed are needed to develop tornadic

severe weather in the warm season (Davies 2006). Cool

season tornadic severe weather, however, can be

instigated by the coupling of significantly lower values of

CAPE with relatively high SRH values.

Figure 1 displays the Peachtree City, Georgia WFO

(KFFC) sounding at 00Z from March 15, 2008.

Peachtree City is located about 30 miles south of the

metropolitan Atlanta area. The 00Z sounding

corresponds to a local time of 8:00pm, about 90 minutes

prior to the touchdown of the tornado at 9:38pm. The

sounding indicated a modest CAPE value of 1030 J/kg.

The 0-3 km SRH values were found to be relatively high

at 215 m2/s

2. Therefore, this situation was characteristic

of a cool season storm development with the low,

modest CAPE values coupled with the high SRH values.

Figure 1: KFFC Sounding for 00Z March 15 2008.

Another informative value to look at is the 0-3 km low-

level CAPE. Moderately high and concentrated CAPE

values over 100 J/kg in the lower levels is a good

indicator of low-level buoyancy and indicates sufficient

warm, moist air for tornado development (Guyer and

Imy 2006). The 0-3 km CAPE value from the KFFC

sounding at 00Z was 106 J/kg. This CAPE value in the

low levels confirmed that this tornado has a good source

of low level moisture. The fairly low LCL level located at

759 m further indicated warm, moist low levels (Guyer

and Imy 2006)

Additional severe weather indices which indicated a

marginally severe environment included the Lifted Index

(LI) at -4.5 and the SWEAT index of 302.8 (Rauber et al.,

2005). A peculiar condition to note is the lack of strong

directional shear in the sounding. The KFFC sounding

displayed westerly winds throughout the vertical with

only changes in wind speeds. These changes were,

however, dramatic, with the speed increasing from 30-

40 kts in the low levels to over 100 kts at upper levels.

The March 14th 12Z (8am local time) and March 15

th

00Z (8pm local time) surface charts as seen in Figure 2

and Figure 3, respectively, set up the synoptic situation

just prior to the tornado touchdown. A large low

pressure system initially located in the Arkansas –

Tennessee area at 12Z moved into the Virginia area by

00Z trailing a cold front.

Figure 2. 12Z March 14 2008: Surface Chart

Careful examination of the 12Z chart in Figure 2 shows

the two supercells starting to form in extreme eastern

Alabama and western Georgia just ahead of the trailing

cold front.

Figure 3. 00Z March 15 2008: Surface Chart

The 500 mb heights and temperature plots for 12Z on

the 14th

and 00Z on the 15th are presented in Figures 4

and 5 below. Cool season tornadoes are known to

exhibit somewhat weak 500mb shortwave troughs as is

seen in Figure 4. The deep 500mb troughs that aid in

the formation of supercells and tornadoes during the

warm season are often absent in the cool season

(Guyer and Imy, 2006).

Figure 4. 12Z March 14 2008: 500 mb Chart

Figures 4 and 5 also show a minor shortwave trough

moving to the east as well as a strong upper level jet

across the gulf states.

Figure 5. 00Z 15 March 2008: 500 mb Chart

The short wave progression from the Louisiana and

Mississippi area into Alabama and Georgia area is even

more evident in the 700 mb plots shown in Figures 6

and 7. Also, strong midlevel winds are apparent in both

charts over the affected area.

Figure 6. 12Z March 14 2008: 700 mb Chart

Mid level moisture, apparent in the 12Z 700 mb chart, in

the Alabama and south Georgia area can be seen to be

absent in the 00Z midlevel chart. This drying is

reflected in the large extent of dry air shown at midlevels

in the KFFC 00Z sounding shown in Figure 1.

Figure 7. 12Z 15 March 2008: 700 mb Chart

The lower level, 850 mb heights, temperatures and

dewpoints are shown for 00Z and 12Z in Figures 8 and

9 below.

Figure 8. 12Z March 14 2008: 850 mb Chart

Figures 8 and 9 depict a moderately strong subtropical

jet with wind speeds of 30 to 35 kts over the area

supplying sufficient moisture ahead of the cold front.

Comparison of the 12Z and 00Z plots shows a

substantial increase in low level moisture ahead of the

cold front in the hours prior to initiation of the supercells.

Figure 9. 00Z 15 March 2008: 850 mb Chart

3 RADAR DATA

NEXRAD radar data, obtained from the Level II archives

at NCDC, was used to track the supercell movement.

Initial observations of the cells movement and

development were made using GR2Analyst software.

A plot of storm relative velocity in Figure 10 shows that

the two main supercells demonstrated a significant level

of low level rotation even as they entered the northwest

part of Georgia. Figure 11 depicts the low level (0.5

degree elevation) reflectivity of the lead tornadic

supercell just prior to touchdown at 0127Z. The cell

shows a pronounced hook and a clear inflow notch.

The corresponding plot of low level storm relative

velocity, Figure 12, shows a fairly well defined rotational

couplet corresponding well to the hook and notch areas.

Figure 10. Storm Relative Velocity at 0102Z

Figure 11. Reflectivity of tornadic cell at 0127Z

Figure 12. Storm relative velocity of tornadic cell at 0127Z

Figure 13. Low level reflectivity and TVS of tornadic cell at 0138Z

Figure 13 shows the low level reflectivity at 0138Z,

about 8 minutes after a tornado warning was issued,

and just about the time of the first reports of the tornado

were received Again, the pronounced inflow region is

easily seen just to the right of the TVS symbol, a green

triangle, plotted by GR2Analyst.

At 9:40pm, the TVS signature continued to moved

eastward through the heart of downtown Atlanta. When

the tornado lifted, it had made a path up to 200 yards

wide and 6 miles long.

4 NLDN LIGHTNING DATA

Both Cloud to Ground (CG) and Cloud to Cloud (CC)

lightning flash and stroke data was acquired from

Vaisala’s National Lightning Detection Network (NLDN).

Data was obtained for the state of Georgia covering the

days of March 14th

and 15th

, 2008.

The NLDN consists of over 100 remote, ground-based

IMPACT sensors broadly distributed over the

continental United States. These sensors

collaboratively detect the location of lightning events

using both direction finding (DF) and time of arrival

(TOA) methods. Typically between 6 and 8 sensors are

used to detect each event.

The data from each IMPACT sensor is collected at the

National Collection Center (NCC) located in Tucson,

Arizona. At the NCC, the data is analyzed to produce

data sets consisting of stroke location, time, polarity,

amplitude and type. Additionally, strokes are grouped

into flashes based on temporal and spatial constraints.

CG flash detection efficiency with the NLDN approaches

95 percent, with a median location accuracy of 500

meters. CC events, typically one or two per flash, are

detected at a lower efficiency, on the order of 10 to 20

percent. In this study, comparisons were typically made

between CG and CC rates between different storms.

This allows the difference in detection efficiencies to

play a lesser role than if the CC and CG rates were

compared within a single storm.

5 SCIT MODIFICATIONS AND DATA

INTEGRATION

The temporal correlation of lightning and tornadic

potential in storm cells has been evaluated using a

slightly modified SCIT algorithm associated with the

appropriate NLDN data. The basic SCIT algorithm was

developed to identify, characterize, and track individual

storm cells in NEXRAD data sets (Johnson, et.al., 1998).

One of the features of the original SCIT algorithm was

its use of seven different reflectivity thresholds. This

study benefited from the use of a smaller set of lower

reflectivity thresholds. The use of lower thresholds

resulted in identified storm cells containing a larger area.

This larger area allowed the inclusion of more lightning

strikes for each identified storm. This, in turn, lead to

better accuracy in evaluating lightning strike frequency,

strength, polarity, and cloud-cloud vs. cloud-ground

lightning strikes over time. It also decreased the

likelihood that an identified storm cell would be

improperly tracked. Although some loss in spatial

resolution results from this modification, the

corresponding benefit is greater temporal accuracy.

The modified SCIT algorithm was run on quality

controlled (QC) reflectivity data output by a neural

network in the WDSS-II system (Lakshmanan, et. al.,

2007). The use of QC reflectivity data is important

because much of the high reflectivity returns that result

from non-precipitating targets such as birds, planes, and

ground clutter has been removed. Without this removal

these non-meteorological targets might be identified as

storm cells. The worst case scenario is that such a non-

meteorological target is contained in a true storm area.

According to the SCIT algorithm, the true storm cell

would then be discarded in order to store the higher

reflectivity “storm cell.” Thus, QC reflectivity not only

prevents false storm cells from being detected, but also

prevents true storm cells from being thrown away.

5.1 Initial NLDN and NEXRAD Correlation using

WDSS-II

The WDSS-II system includes components which allow

the user to ingest both NLDN lightning flash data and

NEXRAD radar data and to present both of these data

sets in a temporally correlated display. An example of

the integration of these two data sets for the March 14-

15 event is shown in Figure 14. Observing the two data

sets correlated in time gave a basic confirmation that

the lightning data appeared to be well correlated to the

two main supercells.

The WDSS-II integration method, while useful for

observing both data sets and their temporal evolution,

proved to be somewhat difficult to use when attempting

to automatically associate lightning flashes with

individual storms. The SCIT numbers produced by

WDSS-II tended to not remain associated with the same

cell as time progressed.

A secondary problem when using the SCIT output from

WDSS-II was that the SCIT itself was represented by a

single point with some associated parameters. Typically

earlier studies have addressed this issue by using an

association radius to correlate lightning flash events with

SCITs. This method seemed somewhat arbitrary and

might sometimes lead to erroneous associations.

Figure 14. WDSS-II & NLDN Correlation

5.2 SCIT Processing

In an effort to address these concerns, as well as to

produce irregularly shaped SCIT areas representative of

the radar reflectivity values, a number of adjustments

were made to the standard SCIT algorithm. These

adjustments were designed to improve its performance

in this specific situation, the tracking of several isolated

supercells in the area of interest.

First, a set of adjustable criteria for identifying 2-D storm

cells (storm cells located in a single elevation slice) was

added to increase the probability that a significant storm

far from the radar would be properly identified. The

increase in vertical distance between sample radar

returns increases with range and thus a storm becomes

less likely to be detected in multiple elevation slices.

The inputs included a required minimum range,

minimum mass, and minimum maximum reflectivity

factor of the 2-D component.

The second adjustment made to the standard SCIT

algorithm involved the discarding of lower reflectivity

thresholds. The conventional SCIT algorithm only

retains the inner most and highest reflectivity

components. Any lower reflectivity component that

contains a higher reflectivity component is discarded

and the highest reflectivity component is stored.

However, the current search method involves searching

for any higher reflectivity component that has its azimuth

extent within that of the lower reflectivity value and its

range extent within its current value. There are cases

where this would result in an improper deletion of a

storm cell. Thus a distance criterion was used to

identify the single component that should be deleted.

The most significant addition to the SCIT algorithm was

the creation of a plan view profile of the shape of each

SCIT identified. The standard SCIT algorithm only

records an estimate of the SCIT region. In the enhanced

SCIT processing, a plan view profile was created by

taking the union of its individual 2-D components.

Furthermore, an option of scaling each 2-D component

about its centroid was made available. This allowed for

small errors in the location of phenomena that occur

near the SCIT perimeter. Once the 2-D components

were scaled, the overlapping components were united

into a single shape profile. In some cases components

did not overlap in the X-Y plane (parallel to Earth’s

surface). In this case the convex hull of the 2

components was taken including any area between the

two components.

5.3 SCIT/NLDN Association

Once the plan profile of each SCIT was created,

lightning flashes were spatially associated with the

appropriate SCIT. Lightning frequency and types were

then plotted versus time. The study was narrowed only

to include SCITs detected for an extended period of

time, more than 20 consecutive scans, in order to

include only the most significant and more developed

storm cells.

A plot showing four detected SCITs, three of which were

detected for greater than 20 scans, and the lightning

flashes associated with each SCIT for a single radar

scan is shown in Figure 15. The black ‘blob’ to the right

is the areal SCIT representing the tornadic supercell,

while the slightly smaller pink ‘blob’ to it’s immediate left

is the areal SCIT representing the second non-tornadic

supercell. The third, red ‘blob’ farther to the left

represents a smaller trailing thunderstorm. The small

yellow ‘+’ signs within each ‘blob’ indicate the reported

location of individual NLDN flashes.

Figure 15. MATLAB SCIT / NLDN Association

6 RESULTS

The MATLAB based SCIT tracking routine followed 3

cells, the tornadic supercell, the non-tornadic supercell,

and a third smaller cell, over more than 20 NEXRAD

scans as they moved across the state of Georgia.

Lightning flash data with locations which corresponded

to each of these three cells was collected and analyzed.

These analyses are presented below. In each of the

plots, the non-tornadic cell is represented by a blue line,

the tornadic cell is represented by a green line, and the

smaller trailing cell is represented by a red line. In each

plot, the radar scan number which most closely

corresponded to the time of reported tornado touchdown

is indicated by a filled triangle on the green, tornadic cell

line.

6.1 SCIT Maximum Reflectivity

In Figure 16, the maximum reflectivity, in dBZ, is plotted

for all three cells as a function of radar scan number.

The two larger supercells, plotted in blue and green,

show similar levels of maximum reflectivity, while the

small cell, plotted in red, initially showed a significantly

lower maximum reflectivity level. No significant change

in maximum reflectivity within the tornadic or non-

tornadic SCIT areas can be seen corresponding to

tornado genesis or touchdown.

Figure 16. SCIT Maximum Reflectivity

6.2 Total Flash Rates

CG lightning has been associated with tornadogenesis,

with peaks in CG rates observed prior to tornado

formation and a relative decrease in conjunction with

touchdown (Perez, et. al, 1997). Similar “lightning

jumps” have been found for “total lightning”, the

combination of CG and CC lightning within a storm

(Goodman, et al. 2005). It is now thought that the

lightning jump prior to the formation of the tornado may

be related to the strong updrafts found during this stage

of storm development (Deierling 2008a, 2008b). Initial

work to characterize the lightning jump phenomenon

has shown some correlation to severe weather events

and may be used with spaceborne detection of total

lightning on the Geostationary Lightning Mapper (GLM)

(Schultz 2008).

Total flash rate, as used in this study, is the combined

rate of CG and CC flashes detected within each SCIT

area during each radar scan interval. A plot of this flash

rate for each of the three cells studied is shown below in

Figure 17. The plot for the large, tornadic supercell

shows a larger overall rate for this storm cell. The

tornadic cell total flash rate also shows a significant

reduction just prior to tornado touchdown. This is

followed by a significant increase shortly after

touchdown.

There seem to be at least one other large dip in total

flash rate for this cell later in its lifecycle unrelated to the

tornado. The second, non-tornadic cell also shows a

smaller, but still distinct, dip in total flash rate at almost

the same time as the dip was observed in the tornadic

cell. The overall rate of total flashes is significantly

smaller for the other two, non-tornadic cells.

Figure 17. Total Flash Characteristics

6.3 CG and CC Flash Rates

Figures 18 and 19 below show CG and CC flashes

detected within each SCIT as a function of radar scan

number. There are interesting differences between the

two figures. The CG rate for the tornadic cell starts at a

relatively high rate and decreases slowly to a more

moderate level. The non-tornadic cell exhibits a similar

moderate level throughout its lifecycle. The smaller cell

shows a much lower CG flash rate than either of the two

supercells.

Figure 18. CG Flashes per Scan

Figure 19, depicting the CC flashes within each SCIT,

exhibits significant differences between the non-tornadic

and the tornadic supercells. The tornadic cell CC flash

rate is much higher than the non-tornadic rate over the

entire lifetime of the storms, although some of this

difference may be due to the difference in areal extent

of the two SCITs. Additionally, the tornadic cell shows a

dramatic decrease in CC rate just prior to and during

tornado touchdown. Both supercells show increases in

CC rates in the middle portions of the storm evolution.

Figure 19. CC Flash Rates

6.4 Positive and Negative Flash Rates

Another aspect of the lightning characteristics examined

was the difference between positive and negative

polarity flash rates between the three cells. In both of

the figures below, the CC and CG flashes are combined

and only the polarity of the flash is used to differentiate

the events.

Figure 20 shows the number negative flashes

associated with each SCIT as a function of radar scan

number. While a possible dip in the number of negative

flashes may have occurred for the tornadic cell, the data

is rather noisy and similar dips can be seen at other

times as well. Generally, the negative flash rate for both

supercells looks similar, while the small cell shows a

much lower rate.

Figure 20. Negative Flash Rates

The positive combined CG and CC flash rate for the

three cells is shown in Figure 21. In this case, the

tornadic cell again shows a much higher rate than the

other two cells. Additionally, the tornadic cell flash rate

decreases immediately prior to touchdown and then

exhibits a dramatic increase after touchdown.

Figure 21. Positive Flash Rates

6.5 Mean Flash Magnitudes

The mean flash magnitude for all type of flashes, both

CC and CG and both negative and positive, occurring

within each SCIT area plotted as a function of radar

scan number is shown in Figure 22. The mean flash

magnitude for the tornadic cell appears, in general, to

be lower than the mean magnitudes for the other two

cells. There may be some indication of a small increase

in magnitude for the tornadic cell just prior to and during

tornado touchdown, followed by a longer period of lower

magnitudes.

Figure 22. Mean Magnitude of Flashes

7 CONCLUSIONS

This event was characterized more by the “cool season”

scenario for tornadic development in the southeastern

United States. The storms developed late in the

evening as relatively low-topped supercells in an

environment with only modest CAPE (just above 1000

J/kg). A critical factor in the development of the storms

was adequate low level moisture and a large amount of

vertical speed shear, with a 0-3 km SRH over 200m2/s

2.

The association of the NLDN flash data with the SCITs

showed interesting differences between the two large

supercells. The tornadic cell had a significant decrease

in both CC and positive flashes just prior to and during

the tornado touchdown. There were some differences

in overall rates between the tornadic and non-tornadic

storms, with the tornadic storm having generally larger

rates of both positive and CC flashes. Some of the

difference in overall rates could be due to some

differences in cell size.

Ongoing work in this area should result in improvements

in the SCIT algorithm and better association with the

NLDN data. Areas being explored include using more

robust methods of special clustering for SCIT

determination and temporal correlation for SCIT tracking.

Additionally, normalization of the NLDN data by SCIT

volume will be included.

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